Featured Publications
SDPRX: A statistical method for cross-population prediction of complex traits
Zhou G, Chen T, Zhao H. SDPRX: A statistical method for cross-population prediction of complex traits. American Journal Of Human Genetics 2022, 110: 13-22. PMID: 36460009, PMCID: PMC9892700, DOI: 10.1016/j.ajhg.2022.11.007.Peer-Reviewed Original ResearchConceptsStatistical methodsJoint distributionWide association study (GWAS) summary statisticsNon-European populationsReal traitsSummary statisticsCross-population predictionPrediction accuracyGenome-wide association study summary statisticsLinkage disequilibrium differencesPrediction performancePolygenic risk scoresComplex traitsStatisticsSimulationsApplicationsTraitsA fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics
Zhou G, Zhao H. A fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics. PLOS Genetics 2021, 17: e1009697. PMID: 34310601, PMCID: PMC8341714, DOI: 10.1371/journal.pgen.1009697.Peer-Reviewed Original ResearchConceptsBayesian nonparametric methodParameter tuningNonparametric methodsExternal reference panelSummary statisticsComputational resourcesParallel algorithmBlock structureExplicit assumptionsExisting methodsStatisticsSeparate validation dataAccurate risk prediction modelsAssumptionPrediction modelPredictionAlgorithm
2021
Hematopoietic mosaic chromosomal alterations increase the risk for diverse types of infection
Zekavat SM, Lin SH, Bick AG, Liu A, Paruchuri K, Wang C, Uddin MM, Ye Y, Yu Z, Liu X, Kamatani Y, Bhattacharya R, Pirruccello JP, Pampana A, Loh PR, Kohli P, McCarroll SA, Kiryluk K, Neale B, Ionita-Laza I, Engels EA, Brown DW, Smoller JW, Green R, Karlson EW, Lebo M, Ellinor PT, Weiss ST, Daly MJ, Terao C, Zhao H, Ebert B, Reilly M, Ganna A, Machiela M, Genovese G, Natarajan P. Hematopoietic mosaic chromosomal alterations increase the risk for diverse types of infection. Nature Medicine 2021, 27: 1012-1024. PMID: 34099924, PMCID: PMC8245201, DOI: 10.1038/s41591-021-01371-0.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAgedAged, 80 and overAgingBiological Specimen BanksChromosome AberrationsCommunicable DiseasesDigestive System DiseasesFemaleGenetic Predisposition to DiseaseGenome-Wide Association StudyGenotypeHematologic NeoplasmsHumansMaleMiddle AgedMosaicismPneumoniaRisk FactorsSepsisUrogenital AbnormalitiesYoung AdultConceptsMosaic chromosomal alterationsLeukocyte cell countDominant risk factorChromosomal alterationsBlood-derived DNAInfectious disease riskIncident infectionsSystem infectionGenitourinary infectionsImmune cellsRisk factorsHematological malignanciesHematological cancersCell countDisease riskInfectionInfectious diseasesClonal hematopoiesisSomatic variantsAgeRiskAlterationsWide association studyAutosomal mosaic chromosomal alterationsAssociation studies
2019
A statistical framework for cross-tissue transcriptome-wide association analysis
Hu Y, Li M, Lu Q, Weng H, Wang J, Zekavat SM, Yu Z, Li B, Gu J, Muchnik S, Shi Y, Kunkle BW, Mukherjee S, Natarajan P, Naj A, Kuzma A, Zhao Y, Crane PK, Lu H, Zhao H. A statistical framework for cross-tissue transcriptome-wide association analysis. Nature Genetics 2019, 51: 568-576. PMID: 30804563, PMCID: PMC6788740, DOI: 10.1038/s41588-019-0345-7.Peer-Reviewed Original ResearchConceptsTranscriptome-wide association analysisAssociation analysisGene-trait associationsGene expression dataGene expression levelsGenetic architectureComplex traitsMore genesGene expressionSingle tissueExpression dataAssociation resultsExpression levelsPowerful approachImputation modelHuman tissuesImputation accuracyGenotypesStatistical frameworkTissueGenesKey componentTraitsPowerful metricExpression
2018
Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders
Walters RK, Polimanti R, Johnson EC, McClintick JN, Adams MJ, Adkins AE, Aliev F, Bacanu SA, Batzler A, Bertelsen S, Biernacka JM, Bigdeli TB, Chen LS, Clarke TK, Chou YL, Degenhardt F, Docherty AR, Edwards AC, Fontanillas P, Foo JC, Fox L, Frank J, Giegling I, Gordon S, Hack LM, Hartmann AM, Hartz SM, Heilmann-Heimbach S, Herms S, Hodgkinson C, Hoffmann P, Jan Hottenga J, Kennedy MA, Alanne-Kinnunen M, Konte B, Lahti J, Lahti-Pulkkinen M, Lai D, Ligthart L, Loukola A, Maher BS, Mbarek H, McIntosh AM, McQueen MB, Meyers JL, Milaneschi Y, Palviainen T, Pearson JF, Peterson RE, Ripatti S, Ryu E, Saccone NL, Salvatore JE, Sanchez-Roige S, Schwandt M, Sherva R, Streit F, Strohmaier J, Thomas N, Wang JC, Webb BT, Wedow R, Wetherill L, Wills AG, Boardman J, Chen D, Choi D, Copeland W, Culverhouse R, Dahmen N, Degenhardt L, Domingue B, Elson S, Frye M, Gäbel W, Hayward C, Ising M, Keyes M, Kiefer F, Kramer J, Kuperman S, Lucae S, Lynskey M, Maier W, Mann K, Männistö S, Müller-Myhsok B, Murray A, Nurnberger J, Palotie A, Preuss U, Räikkönen K, Reynolds M, Ridinger M, Scherbaum N, Schuckit M, Soyka M, Treutlein J, Witt S, Wodarz N, Zill P, Adkins D, Boden J, Boomsma D, Bierut L, Brown S, Bucholz K, Cichon S, Costello E, de Wit H, Diazgranados N, Dick D, Eriksson J, Farrer L, Foroud T, Gillespie N, Goate A, Goldman D, Grucza R, Hancock D, Harris K, Heath A, Hesselbrock V, Hewitt J, Hopfer C, Horwood J, Iacono W, Johnson E, Kaprio J, Karpyak V, Kendler K, Kranzler H, Krauter K, Lichtenstein P, Lind P, McGue M, MacKillop J, Madden P, Maes H, Magnusson P, Martin N, Medland S, Montgomery G, Nelson E, Nöthen M, Palmer A, Pedersen N, Penninx B, Porjesz B, Rice J, Rietschel M, Riley B, Rose R, Rujescu D, Shen P, Silberg J, Stallings M, Tarter R, Vanyukov M, Vrieze S, Wall T, Whitfield J, Zhao H, Neale B, Gelernter J, Edenberg H, Agrawal A. Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders. Nature Neuroscience 2018, 21: 1656-1669. PMID: 30482948, PMCID: PMC6430207, DOI: 10.1038/s41593-018-0275-1.Peer-Reviewed Original ResearchConceptsGenetic underpinningsGenome-wide association studiesGenome-wide dataLarge genome-wide association studiesGenome-wide significant effectComplex polygenic architectureSignificant genetic correlationsPolygenic architectureGenetic distinctionCommon genetic underpinningsAssociation studiesGenetic relationshipsGenetic correlationsGenetic ancestryFamily-based studyUse of cigarettesAttention deficit hyperactivity disorder
2017
Network Clustering Analysis Using Mixture Exponential-Family Random Graph Models and Its Application in Genetic Interaction Data
Wang Y, Fang H, Yang D, Zhao H, Deng M. Network Clustering Analysis Using Mixture Exponential-Family Random Graph Models and Its Application in Genetic Interaction Data. IEEE/ACM Transactions On Computational Biology And Bioinformatics 2017, 16: 1743-1752. PMID: 28858811, DOI: 10.1109/tcbb.2017.2743711.Peer-Reviewed Original ResearchConceptsExponential-family random graph modelsRandom graph modelsGraph modelStatistical network modelsHeterogeneity of networksLarge-scale genetic interaction networksReal social networksERGM parametersSubset of nodesOnline graphStatistical modelData sizeObserved networkEM algorithmNetwork informationGraph nodesMixture problemSocial networksFlexible wayNetwork modelNetwork clustersClassical methodsIncredible setInteraction dataNetworkJoint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction
Hu Y, Lu Q, Liu W, Zhang Y, Li M, Zhao H. Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction. PLOS Genetics 2017, 13: e1006836. PMID: 28598966, PMCID: PMC5482506, DOI: 10.1371/journal.pgen.1006836.Peer-Reviewed Original Research
2014
GPA: A Statistical Approach to Prioritizing GWAS Results by Integrating Pleiotropy and Annotation
Chung D, Yang C, Li C, Gelernter J, Zhao H. GPA: A Statistical Approach to Prioritizing GWAS Results by Integrating Pleiotropy and Annotation. PLOS Genetics 2014, 10: e1004787. PMID: 25393678, PMCID: PMC4230845, DOI: 10.1371/journal.pgen.1004787.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesFunctional annotationGWAS datasetsAnnotation informationStatistical approachMultiple GWAS datasetsGenome-wide markersPowerful statistical methodsSingle-phenotype analysisCentral nervous system genesRisk variantsNervous system genesGenotype-Tissue Expression (GTEx) databaseComplex diseasesGWAS data setsSignificant pleiotropic effectsCommon risk basisDifferent complex diseasesDNase-seq dataCell linesStatistical inferenceGenetic architectureGWAS hitsGWAS resultsNovel statistical approach
2005
Response to Dr. Kopke's comments on haplotypes at the OPRM1 locus
Luo X, Gelernter J, Zhao H, Kranzler HR. Response to Dr. Kopke's comments on haplotypes at the OPRM1 locus. American Journal Of Medical Genetics Part B Neuropsychiatric Genetics 2005, 135B: 102-102. PMID: 15806579, DOI: 10.1002/ajmg.b.30060.Peer-Reviewed Original Research
2001
The Power of Transmission Disequilibrium Tests for Quantitative Traits
Li J, Wang D, Dong J, Jiang R, Zhang K, Zhang S, Zhao H, Sun F. The Power of Transmission Disequilibrium Tests for Quantitative Traits. Genetic Epidemiology 2001, 21: s632-s637. PMID: 11793752, DOI: 10.1002/gepi.2001.21.s1.s632.Peer-Reviewed Original Research
1999
On a Randomization Procedure in Linkage Analysis
Zhao H, Merikangas K, Kidd K. On a Randomization Procedure in Linkage Analysis. American Journal Of Human Genetics 1999, 65: 1449-1456. PMID: 10521312, PMCID: PMC1288298, DOI: 10.1086/302607.Peer-Reviewed Original ResearchConceptsEfficient simulation procedureObserved test statisticSimulation-based methodTheoretical resultsTest statisticNovel simulation methodSimulation methodReal dataSimulation procedureUninformative markersTheoretical workStatistical testsPedigree structureGenomewide significance levelRandomization procedureDiabetes dataStatisticsA more powerful method to evaluate p‐values in GENEHUNTER
Zhao H, Sheffield L, Pakstis A, Knauert M, Kidd K. A more powerful method to evaluate p‐values in GENEHUNTER. Genetic Epidemiology 1999, 17: s415-s420. PMID: 10597472, DOI: 10.1002/gepi.1370170770.Peer-Reviewed Original Research
1997
The Effects of Genotyping Errors and Interference on Estimation of Genetic Distance
Goldstein D, Zhao H, Speed T. The Effects of Genotyping Errors and Interference on Estimation of Genetic Distance. Human Heredity 1997, 47: 86-100. PMID: 9097090, DOI: 10.1159/000154396.Peer-Reviewed Original Research